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Short-term prediction of urban PM2.5 based on a hybrid modified variational mode decomposition and support vector regression model.
Chu, Junwen; Dong, Yingchao; Han, Xiaoxia; Xie, Jun; Xu, Xinying; Xie, Gang.
Afiliación
  • Chu J; Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
  • Dong Y; Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
  • Han X; Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China. hanxiaoxia@tyut.edu.cn.
  • Xie J; College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
  • Xu X; Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
  • Xie G; Department of Automation, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
Artículo en Inglés | MEDLINE | ID: mdl-33044693
ABSTRACT
PM2.5 (particulate matter with a size/diameter ≤ 2.5 µm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM2.5 are non-linear and non-stationary, it is difficult to predict future PM2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM2.5 prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM2.5 data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques.
Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio pronóstico / Factores de riesgo Idioma: Inglés Asunto de la revista: Salud Ambiental / Toxicología Año: 2020 Tipo del documento: Artículo País de afiliación: China

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio pronóstico / Factores de riesgo Idioma: Inglés Asunto de la revista: Salud Ambiental / Toxicología Año: 2020 Tipo del documento: Artículo País de afiliación: China
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